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6,452 results for “Application”
Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.
Key Real-World Applications of Classifier Ensembles
Broad classes of statistical classification algorithms have beendeveloped and applied successfully to a wide range of real worlddomains. In general, ensuring that the particular classificationalgorithm matches the properties of the data is crucial inproviding results that meet the needs of the particular applicationdomain. One way in which the impact of this algorithm/applicationmatch can be alleviated is by using ensembles of classifiers, wherea variety of classifiers (either different types of classifiers ordifferent instantiations of the same classifier) are pooled before afinal classification decision is made. Intuitively, classifierensembles allow the different needs of a difficult problem to behandled by classifiers suited to those particular needs.Mathematically, classifier ensembles provide an extra degree offreedom in the classical bias/variance tradeoff, allowing solutionsthat would be difficult (if not impossible) to reach with only asingle classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we surveyselect applications of ensemble methods to problems that havehistorically been most representative of the difficulties inclassification. In particular, we survey applications of ensemblemethods to remote sensing, person recognition, one vs. allrecognition, and medicine.
Topic Modeling for OLAP on Multidimensional Text Databases: Topic Cube and its Applications
As the amount of textual information grows explosively in various kinds of business systems, it becomes more and more desirable to analyze both structured data records and unstructured text data simultaneously. Although online analytical processing (OLAP) techniques have been proven very useful for analyzing and mining structured data, they face challenges in handling text data. On the other hand, probabilistic topic models are among the most effective approaches to latent topic analysis and mining on text data. In this paper, we study a new data model called topic cube to combine OLAP with probabilistic topic modeling and enable OLAP on the dimension of text data in a multidimensional text database. Topic cube extends the traditional data cube to cope with a topic hierarchy and stores probabilistic content measures of text documents learned through a probabilistic topic model. To materialize topic cubes efficiently, we propose two heuristic aggregations to speed up the iterative Expectation-Maximization (EM) algorithm for estimating topic models by leveraging the models learned on component data cells to choose a good starting point for iteration. Experimental results show that these heuristic aggregations are much faster than the baseline method of computing each topic cube from scratch. We also discuss some potential uses of topic cube and show sample experimental results.
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data driven methods. Standard procedures are used by air traffic controllers (ATC) to guide aircraft, ensure the safety of the airspace, and to maximize the runway occupancy. Even though standard procedures are used by ATC, the control of the aircraft remains with the pilots, leading to a large variability in the flight patterns observed. Two methods to identify typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to the typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure a safe separation between aircraft.
Application of Model Based Prognostics to Pneumatic Valves in a Cryogenic Propellant Loading Testbed
Pneumatic-actuated valves are critical components in many applications, including cryogenic propellant loading for space operations. For these components, failures need to be predicted so that components can be repaired to ensure mission success, i.e., health monitoring and fault prognostics is required. In order to develop, test, mature, and deploy valve prognostics algorithms, we have developed a testbed for pneumatic valves used in cryogenic service for propellant loading operations, in which we can inject controlled damage pro files and observe its effects on valve operation. In this paper, we focus on the prognostics of a continuously-controlled pneumatic valve. We describe the construction of the testbed, the fault injection mechanisms, and the model-based valve prognostics algorithms. Experimental results from the testbed demonstrate successful prediction of valve failure.
Trajectory Clustering with Applications to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft. IEEE Transactions on Intelligent Transportation Systems, Dec. 2011, 12(4), pp. 1511 - 1524.
Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications
Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally dif- ficult to seed particular sensor faults. Therefore, research is un- derway to better understand the different fault modes seen in sen- sors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper il- lustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vi- bration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classi- fier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and dis- ambiguation efficacy with respect to severity of fault conditions.
Application of Inductive Monitoring System to Plug Load Anomaly Detection
NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. LEED Platinum office building. Plug loads are expected to account for a significant portion of the overall energy consumption. This is because building design choices have resulted in greatly reduced energy demand from Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are major contributors to energy consumption in traditional buildings. In anticipation of the importance of plug loads in Sustainability Base, a pilot study was conducted to collect data from a variety of plug loads. A number of cases of anomalous or unhealthy behavior were observed including schedule-based rule failures, time-to-standby errors, changed loads, and inter-channel anomalies. These issues prevent effective plug load management; therefore, they are important to promptly identify and correct. The Inductive Monitoring System (IMS) data mining algorithm was chosen to identify errors. This paper details how an automated data analysis program was created, tested and implemented using IMS. This program will be applied to Sustainability Base to maintain effective plug load management system performance, identify malfunctioning equipment, and reduce building energy consumption.
Novel applications of next generation sequencing tools to assess the health of Steller sea lion (Eumetopias jubatus) populations
GEO Series GSE100883. Eumetopias jubatus. 13 samples. Type: Expression profiling by high throughput sequencing.
Prognostic and Biologic Relevance of Clinically Applicable Long Non-Coding RNA Profiling in Older Patients with Cytogenetically Normal AML
GEO Series GSE130923. Homo sapiens. 99 samples. Type: Other.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma
GEO Series GSE296948. Homo sapiens. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
A new genome wide expression array and its application to eQTL mapping, synergy to other platforms
GEO Series GSE15905. Homo sapiens. 90 samples. Type: Expression profiling by array.
Discovery and application of postnatal nucleus pulposus progenitors essential for intervertebral disc homeostasis and degeneration.
GEO Series GSE192789. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Application of text mining to build AOP-based mucus hypersecretion genesets and validation with in vitro and clinical samples
GEO Series GSE142100. Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing.
Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq
GEO Series GSE92402. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Ribosomal protein S3 mediates drug resistance of proteasome inhibitor: therapeutic application in multiple myeloma
GEO Series GSE240978. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Exosome gene expression technology to understand human health risks in space exploration: applications for SANS
GEO Series GSE199468. Homo sapiens. 46 samples. Type: Expression profiling by high throughput sequencing.
Application of a Agilent hydatid 4x44k - cDNA microarray for identification of dihydroartemisinin targeting genes in Echinococcus granulosus larval protoscoleces
GEO Series GSE184908. Echinococcus granulosus. 6 samples. Type: Expression profiling by array.
BLADE-R: streamlined RNA extraction for molecular diagnostics and high-throughput applications.
GEO Series GSE278095. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
A flexible model for correlated count data, with application to analysis of gene expression differences in multi-condition experiments
GEO Series GSE214633. Mus musculus. 50 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.